TY - GEN
T1 - Farsighted sensor management strategies for move/stop tracking
AU - Nedich, Angelia
AU - Schneider, Michael K.
AU - Washburn, Robert B.
PY - 2005
Y1 - 2005
N2 - We consider the sensor management problem arising in using a multi-mode sensor to track moving and stopped targets. The sensor management problem is to determine what measurements to take in time so as to optimize the utility of the collected data. Finding the best sequence of measurements is a hard combinatorial problem due to many factors, including the large number of possible sensor actions and the complexity of the dynamics. The complexity of the dynamics is due in part to the sensor dwell-time depending on the sensor mode, targets randomly starting and stopping, and the uncertainty in the sensor detection process. For such a sensor management problem, we propose a novel, computationally efficient, farsighted algorithm based on an approximate dynamic programming methodology. The algorithm's complexity is polynomial in the number of targets. We evaluate this algorithm against a myopic algorithm optimizing an information-theoretic scoring criterion. Our simulation results indicate that the farsighted algorithm performs better with respect to the average time the track error is below a specified goal value.
AB - We consider the sensor management problem arising in using a multi-mode sensor to track moving and stopped targets. The sensor management problem is to determine what measurements to take in time so as to optimize the utility of the collected data. Finding the best sequence of measurements is a hard combinatorial problem due to many factors, including the large number of possible sensor actions and the complexity of the dynamics. The complexity of the dynamics is due in part to the sensor dwell-time depending on the sensor mode, targets randomly starting and stopping, and the uncertainty in the sensor detection process. For such a sensor management problem, we propose a novel, computationally efficient, farsighted algorithm based on an approximate dynamic programming methodology. The algorithm's complexity is polynomial in the number of targets. We evaluate this algorithm against a myopic algorithm optimizing an information-theoretic scoring criterion. Our simulation results indicate that the farsighted algorithm performs better with respect to the average time the track error is below a specified goal value.
KW - Farsighted strategy
KW - Sensor management
KW - Stochastic dynamic programming
KW - Tracking
UR - http://www.scopus.com/inward/record.url?scp=33847163744&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33847163744&partnerID=8YFLogxK
U2 - 10.1109/ICIF.2005.1591905
DO - 10.1109/ICIF.2005.1591905
M3 - Conference contribution
AN - SCOPUS:33847163744
SN - 0780392868
SN - 9780780392861
T3 - 2005 7th International Conference on Information Fusion, FUSION
SP - 566
EP - 573
BT - 2005 7th International Conference on Information Fusion, FUSION
PB - IEEE Computer Society
T2 - 2005 8th International Conference on Information Fusion, FUSION
Y2 - 25 July 2005 through 28 July 2005
ER -